使用知识图谱提高RAG的能力，减少大模型幻觉

MATCH (:User {userId: 
‘referenceUser’})-[:FRIEND]->()-[:FRIEND]->(fof:User) RETURN fof

%%capture
 pip install llama_index pyvis Ipython langchain pypdf

import logging
 import sys
 #
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index import (SimpleDirectoryReader,
                          LLMPredictor,
                          ServiceContext,
                          KnowledgeGraphIndex)
 #
 from llama_index.graph_stores import SimpleGraphStore
 from llama_index.storage.storage_context import StorageContext
 from llama_index.llms import HuggingFaceInferenceAPI
 from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
 from llama_index.embeddings import LangchainEmbedding
 from pyvis.network import Network

HF_TOKEN = "api key DEEPHUB 123456"
 llm = HuggingFaceInferenceAPI(
    model_name="HuggingFaceH4/zephyr-7b-beta", token=HF_TOKEN
 )

embed_model = LangchainEmbedding(
  HuggingFaceInferenceAPIEmbeddings(api_key=HF_TOKEN,model_name="thenlper/gte-large")
 )

documents = SimpleDirectoryReader("/content/Documents").load_data()
 print(len(documents))
 
 ####Output###
 44

#setup the service context
 
 service_context = ServiceContext.from_defaults(
    chunk_size=256,
    llm=llm,
    embed_model=embed_model
 )
 
 #setup the storage context
 
 graph_store = SimpleGraphStore()
 storage_context = StorageContext.from_defaults(graph_store=graph_store)
 
 #Construct the Knowlege Graph Undex
 index = KnowledgeGraphIndex.from_documents( documents=documents,
                                            max_triplets_per_chunk=3,
                                            service_context=service_context,
                                            storage_context=storage_context,
                                          include_embeddings=True)

query = "What is ESOP?"
 query_engine = index.as_query_engine(include_text=True,
                                      response_mode ="tree_summarize",
                                      embedding_mode="hybrid",
                                      similarity_top_k=5,)
 #
 message_template =f"""<|system|>Please check if the following pieces of context has any mention of the keywords provided in the Question.If not then don't know the answer, just say that you don't know.Stop there.Please donot try to make up an answer.</s>
 <|user|>
 Question: {query}
 Helpful Answer:
 </s>"""
 #
 response = query_engine.query(message_template)
 #
 print(response.response.split("<|assistant|>")[-1].strip())
 #####OUTPUT #####################
 ESOP stands for Employee Stock Ownership Plan. It is a retirement plan that allows employees to receive company stock or stock options as part of their compensation. In simpler terms, it is a plan that allows employees to own a portion of the company they work for. This can be a motivating factor for employees as they have a direct stake in the company's success. ESOPs can also be a tax-efficient way for companies to provide retirement benefits to their employees.

from pyvis.network import Network
 from IPython.display import display
 g = index.get_networkx_graph()
 net = Network(notebook=True,cdn_resources="in_line",directed=True)
 net.from_nx(g)
 net.show("graph.html")
 net.save_graph("Knowledge_graph.html")
 #
 import IPython
 IPython.display.HTML(filename="/content/Knowledge_graph.html")

storage_context.persist()